Data-Driven Distributed H ∞ Current Sharing Consensus Optimal Control of DC Microgrids via Reinforcement Learning

Distributed control of DC microgrid is becoming more and more important in modern power system. An important control goal is to ensure voltage stability and current sharing of DC bus. In the presence of constant power loads and uncertainties, a novel distributed quadratic optimum control technique based on reinforcement learning (RL) is developed in order to ensure correct current sharing and adequate performance. Firstly, the system model with power coupling is established and transformed into a linear heterogeneous multi-agent system (MAS) with unknown disturbances. Subsequently, a neural network (NN)-based adaptive model-free observer is developed. Since not all followers have direct access to the leader's information, a distributed cooperation performance index with discount component is created by fusing the dynamics of the observer and the follower. The $Q$ -learning technique is applied to obtain the optimal control strategy to achieve voltage stabilization and current sharing without using system dynamics. Finally, simulation and experimental results show the effectiveness of this strategy.